脑电图网络指数作为慢性中风上肢损伤的生物标志物
1Department of Electronics and Information Engineering,Korea University, 2Department of Rehabilitation Medicine, Seoul National University College of Medicine,Seoul National University Bundang Hospital, 3Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology,Korea University
Summary
实验方案展示了在中风患者上肢运动期间获取和分析脑电图（EEG）信号的范式。在受损的上肢运动过程中观察到低β脑电图频段功能网络的改变，并与运动障碍的程度有关。
Abstract
据报道，在受损肢体的特定任务运动期间脑电图 （EEG） 信号的改变是运动障碍严重程度和预测卒中患者运动恢复的潜在生物标志物。在实施脑电图实验时，需要详细的范式和组织良好的实验方案，以获得稳健且可解释的结果。在该协议中，我们说明了一种特定于任务的范式，其中包含上肢运动以及获取和分析脑电图数据所需的方法和技术。该范式包括 1 分钟的休息，然后是 10 次试验，分别在 4 个疗程中交替进行 5 秒和 3 秒的休息和任务（手伸展）状态。使用 32 个 Ag/AgCl 头皮电极以 1,000 Hz 的采样率采集脑电图信号，在低 β （12-20 Hz） 频段进行与肢体运动相关的事件相关频谱扰动分析和全局水平的功能网络分析。代表性结果显示，在受损的上肢运动过程中，低β脑电图频段的功能网络发生了改变，并且功能网络的改变与慢性脑卒中患者的运动障碍程度有关。结果证明了中风患者上肢运动期间脑电图测量实验范式的可行性。需要使用这种范式进行进一步研究，以确定脑电图信号作为运动损伤和恢复的生物标志物的潜在价值。
Introduction
上肢运动障碍是卒中最常见的后果之一，与日常生活活动受限有关1,2。已知 Alpha （8-13 Hz） 和 beta （13-30 Hz） 波段节律与运动密切相关。特别是，研究表明，在肢体受损运动期间，α 和下 β （12-20 Hz） 频段的神经活动改变与中风患者的运动障碍程度相关 3,4,5。基于这些发现，脑电图 （EEG） 已成为一种潜在的生物标志物，它反映了运动障碍的严重程度和运动恢复的可能性 6,7。然而，先前开发的基于脑电图的生物标志物已被证明不足以研究卒中患者的运动障碍特征，这主要是因为它们依赖于静息态脑电图数据而不是任务诱导的脑电图数据8,9,10。与运动障碍相关的复杂信息处理，例如同侧半球和对侧半球之间的相互作用，只能通过任务诱导的脑电图数据来揭示，而不是静息态脑电图。因此，不仅需要进一步的研究来探索神经元活动与运动障碍特征之间的关系，还需要阐明在受损身体部位运动过程中产生的脑电图作为中风患者运动障碍的潜在生物标志物的有用性11.
实施脑电图以评估行为影响需要特定于任务的范式和协议。迄今为止，已经提出了各种脑电图方案12，其中卒中患者进行想象或实际的运动以诱导与运动相关的大脑活动11,13。在想象运动的情况下，大约53.7%的参与者无法明确地想象相应的运动（称为“文盲”），因此未能诱导与运动相关的大脑活动14。此外，严重中风患者很难移动整个上肢，并且由于运动不稳定，在数据采集过程中可能会产生不必要的伪影。因此，需要基于专家专业知识的指导，以获得与任务相关的高质量脑电图数据和神经生理学可解释的结果。在这项研究中，我们为脑卒中患者全面设计了一个实验范式，以执行相对简单的手部运动任务，并提供了具有详细指导的实验程序。
通过概述本文中的可视化实验方案，我们旨在说明使用脑电图系统获取和分析与上肢运动相关的神经元活动的具体概念和方法。为了证明偏瘫性中风参与者的麻痹性和非麻痹性上肢之间通过脑电图进行神经元活动的差异，本研究旨在介绍使用所述方案作为脑电图作为横断面背景下中风患者运动障碍严重程度的潜在生物标志物的可行性。
Protocol
所有实验程序均由首尔大学盆唐医院机构审查委员会审查和批准。在这项研究的实验中，招募了 34 名中风参与者。已获得所有参与者签署的知情同意书。如果参与者符合标准但由于残疾而无法签署同意书，则从法定代表处获得签署的知情同意书。 1. 实验装置 患者招募使用以下纳入标准执行筛选过程：年龄在 18 至 85 岁之间，存在上肢功能受损;<br…
Representative Results
图 7 显示了每个手部运动任务的地形低 beta ERD 图。与同侧半球相比，在受影响和未受影响的手部运动任务中，在对半球观察到显着强的低 β ERD。 图 7：分别执行受影响和未受影响手部运动任务的所有患者的平均地形图。 ?…
Discussion
本研究引入了一项脑电图实验，用于测量中风患者上肢运动相关的神经元活动。应用脑电图采集和分析的实验范式和方法确定同病和对位运动皮层的ERD模式。
ERSP图的结果（图7）表明，当移动受损和未受影响的手时，神经元激活程度的差异。结果与先前文章26,27的结果一致，并表明实验设置是一种可行的方法，</su…
Disclosures
The authors have nothing to disclose.
Acknowledgements
这项工作得到了韩国政府（MSIT）资助的韩国国家研究基金会（NRF）资助（No.NRF-2022R1A2C1006046），由韩国文部科学部资助的韩国国家研究基金会（NRF）脑科学原创技术研究计划（2019M3C7A1031995），由韩国政府资助的韩国国家研究基金会（NRF）（No.NRF-2022R1A6A3A13053491）和韩国MSIT（科学与信息通信技术部）根据ITRC（信息技术研究中心）支持计划（IITP-2023-RS-2023-00258971）由IITP（信息与通信技术规划与评估研究所）监督。
Materials
| actiCAP | Easycap, GmbH Ltd., Herrsching, Germany | CAC-32-SAMW-56 | Textile EEG cap platform to accommodate EEG electrodes |
| Brain Vision Recorder (Software) | Brain Products GmBH Ltd., Munich, Germany | – | Software used to record EEG signal |
| Curry 7 (Software) | Compumedics, Australia | – | Software used in preprocessing of EEG data |
| LiveAmp | Brain Products, GmbH Ltd., Gilching, Germany | LA-055606-0348 | EEG system (amplifier) used for the measurement |
| MATLAB R2019a (Software) | MathWorks Inc., Natick, MA, USA | – | Software used to run the experimental stimulus and analyze the EEG data |
| Recording PC | Lenovo Group Limited, Hong Kong, China | Model: X58K | Intel Core i7-7700HQ CPU@2.80 GHz, RAM 8 GB /EEG data recording using Brain Vision Recorder |
| Sensor&Trigger Extension(STE) | Brain Products GmBH Ltd., Munich, Germany | STE-055604-0162 | Adds physioloigcal signals to the EEG amplifier |
| Splitter box | Brain Products GmBH Ltd., Munich, Germany | BP-135-1600 | Connects Ag/AgCl electrodes to the EEG amplifier |
| Stimulation PC | Hansung Corporation, Seoul, Korea | Model: ThinkPad P71 | Intel Core i7-8750H CPU@2.20 GHz, RAM 8 GB Presenting stimulation screen using MATLAB |
| TriggerBox | Brain Products GmBH Ltd., Munich, Germany | BP-245-1010 | Receives trigger signal from PC and relay it to EEG recording system |
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Cite This Article
Choi, G., Chang, W. K., Shim, M., Kim, N., Jang, J., Kim, W., Hwang, H., Paik, N. Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke. J. Vis. Exp. (197), e64753, doi:10.3791/64753 (2023).